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All Journal International Journal of Electrical and Computer Engineering
Sakli, Hedi
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Computer Science & IT Electrical & Electronics Engineering

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Efficient rectifier with wide input power range for 5G applications Yamna, Mounira Ben; Dakhli, Nabil; Sakli, Hedi; Aoun, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3809-3819

Abstract

This article presents three efficient rectifiers for radio frequency energy harvest-ing (RFEH) systems operating at the fifth generation (5G) band (3.5 GHz). Eachrectifier operates at various input power levels (high, low, and across a widepower range). The high and low-power rectifiers feature a single serial topologyusing HSMS-2860 and SMS-7630 Schottky diodes, respectively, along with mi-crostrip lines to implement the input and output filters and the impedance match-ing network. At an radio frequency (RF) power level of 15 dBm, the high-powerrectifier harvests 67.4% to direct current (DC) power with a 300Ωload resistorand an output voltage of 2.5V. The low-power rectifier achieves its maximumpower conversion efficiency (PCE) at -2 dBm, reaching 45% efficiency with a1200Ωload. The rectifier with a extended input power range comprises twobranches of subrectifiers functioning at both high and low power levels. De-pending on the power level, the considered subrectifier harvests radio frequencypower into DC power, while the other subrectifier is deactivated. Across a powerspan of 32.5 dB (ranging from -13 to 19.5 dBm), the rectifier maintains an effi-ciency above 30%. The proposed rectifiers are efficient and suitable for imple-mentation in 5G-enabled RFEH systems.
Proactive monitoring and predictive alerts for COVID-19 patient management using internet of things, artificial intelligence, and cloud Leila, Ennaceur; Othman, Soufiene Ben; Sakli, Hedi; Yahia, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7266-7274

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has sparked changes across various domains, encompassing health, commerce, education, and the economy. Given the widespread impact of COVID-19 across numerous nations, it has strained hospital resources, oxygen reserves, and healthcare personnel. Consequently, there exists an urgent necessity to exploit sophisticated technologies such as artificial intelligence and the internet of things (IoT) to monitor patients effectively. This scholarly article proposes a prototype that integrates IoT and artificial intelligence (IA) for the surveillance of COVID-19 patients within healthcare facilities. Wearable IoT devices, equipped with embedded sensors, autonomously collect vital information like oxygen levels and body temperature. Notably, oxygen saturation and heart rate serve as significant markers in COVID-19 cases. These metrics are discerned through the deep learning capabilities of the TensorFlow library. The prototype aims to augment the intelligence of IoT sensors to identify these crucial signs through a trained model. A meticulously labeled dataset comprising oxygen saturation and heart rate data is amassed. Deep neural networks are deployed to prognosticate the disease's progression. The utilization of these technologies harbors the potential for rapid advancements in healthcare, thereby mitigating risks to human life and fostering more proactive responses to health crises.
Co-Authors Aoun, Mohamed Dakhli, Nabil Leila, Ennaceur Othman, Soufiene Ben Yahia, Mohamed Yamna, Mounira Ben